Liver Computed Tomographic Perfusion in the Assessment of Microvascular Invasion in Patients With Small Hepatocellular Carcinoma:

Research progresses of imaging studies on preoperative prediction of microvascular invasion of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer, accounting for approximately 90% of liver cancer cases. It currently ranks as the fifth most prevalent cancer worldwide and represents the third leading cause of cancer-related mortality. As a malignant disease with surgical resection and ablative therapy being the sole curative options available, it is disheartening that most HCC patients who undergo liver resection experience relapse within five years. Microvascular invasion (MVI), defined as the presence of micrometastatic HCC emboli within liver vessels, serves as an important histopathological feature and indicative factor for both disease-free survival and overall survival in HCC patients. Therefore, achieving accurate preoperative noninvasive prediction of MVI holds vital significance in selecting appropriate clinical treatments and improving patient prognosis. Currently, there are no universally recognized criteria for preoperative diagnosis of MVI in clinical practice. Consequently, extensive research efforts have been directed towards preoperative imaging prediction of MVI to address this problem and the relative research progresses were reviewed in this article to summarize its current limitations and future research prospects.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.

Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm)

PURPOSE

To evaluate the role of perfusion parameters with MR imaging of the liver in diagnosing MVI in hepatocellular carcinoma (HCC) (between 1 and 5 cm).

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board. In 80 patients with 43 MVI( +) and 42 MVI( -) HCC, whole-liver perfusion MR imaging with Cartesian k-space undersampling and compressed sensing reconstruction was performed after injection of 0.1 mmol/kg gadopentetate dimeglumine. Parameters derived from a dual-input single-compartment model of arterial flow (Fa), portal venous flow (Fp), total blood flow (Ft = Fa + Fp), arterial fraction (ART), distribution volume (DV), and mean transit time (MTT) were measured. The significant parameters between the two groups were included to correlate with the presence of MVI at simple and multiple regression analysis.

RESULTS

In MVI-positive HCC, Fp was significantly higher than in MVI-negative HCC, whereas the reverse was seen for ART (p < 0.001). Tumor size (β = 1.2, p = 0.004; odds ratio, 3.20; 95% CI 1.45, 7.06), Fp (β = 1.1, p = 0.004; odds ratio, 3.09; 95% CI 1.42, 6.72), and ART (β =  - 3.1, p = 0.001; odds ratio, 12.13; 95% CI 2.85, 51.49) were independent risk factors for MVI. The AUC value of the combination of all three metrics was 0.931 (95% CI 0.855, 0.975), with sensitivity of 97.6% and specificity of 76.2%.

CONCLUSION

The combination of Fp, ART, and tumor size demonstrated a higher diagnostic accuracy compared with each parameter used individually when evaluating MVI in HCC (between 1 and 5 cm).

Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT)

Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10−5), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

MVI has significant clinical value for treatment selection and prognosis evaluation in hepatocellular carcinoma (HCC). We aimed to construct a model based on MVI-Related Genes (MVIRGs) for risk assessment and prognosis prediction in patients with HCC. This study utilized various statistical analysis methods for prognostic model construction and validation in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, respectively. In addition, immunohistochemistry and qRT-PCR were used to analyze and identify the value of the model in our cohort. After the analyses, 153 differentially expressed MVIRGs were identified, and three key genes were selected to construct a prognostic model. The high-risk group showed significantly lower overall survival (OS), and this trend was observed in all subgroups: different age groups, genders, stages, and grades. Risk score was a risk factor independent of age, gender, stage, and grade. Moreover, the ICGC cohort validated the prognostic value of the model corresponding to the TCGA. In our cohort, qRT-PCR and immunohistochemistry showed that all three genes had higher expression levels in HCC samples than in normal controls. High expression levels of genes and high-risk scores showed significantly lower recurrence-free survival (RFS) and OS, especially in MVI-positive HCC samples. Therefore, the prognostic model constructed by three MVIRGs can reliably predict the RFS and OS of patients with HCC and is valuable for guiding clinical treatment selection and prognostic assessment of HCC.

A Systematic Review on the Role of the Perfusion Computed Tomography in Abdominal Cancer

Background and purpose

Perfusion Computed Tomography (CTp) is an imaging technique which allows quantitative and qualitative evaluation of tissue perfusion through dynamic CT acquisitions. Since CTp is still considered a research tool in the field of abdominal imaging, the aim of this work is to provide a systematic summary of the current literature on CTp in the abdominal region to clarify the role of this technique for abdominal cancer applications.

Materials and Methods

A systematic literature search of PubMed, Web of Science, and Scopus was performed to identify original articles involving the use of CTp for clinical applications in abdominal cancer since 2011. Studies were included if they reported original data on CTp and investigated the clinical applications of CTp in abdominal cancer.

Results

Fifty-seven studies were finally included in the study. Most of the included articles (33/57) dealt with CTp at the level of the liver, while a low number of studies investigated CTp for oncologic diseases involving UGI tract (8/57), pancreas (8/57), kidneys (3/57), and colon–rectum (5/57).

Conclusions

Our study revealed that CTp could be a valuable functional imaging tool in the field of abdominal oncology, particularly as a biomarker for monitoring the response to anti-tumoral treatment.

Advanced CT techniques for assessing hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma

BACKGROUND

Pre-operative non-invasive histological evaluation of hepatocellular carcinoma (HCC) remains a challenge. Tumor perfusion is significantly associated with the development and aggressiveness of HCC. The purpose of the study was to evaluate the clinical value of quantitative liver perfusion parameters and corresponding histogram parameters derived from traditional triphasic enhanced computed tomography (CT) scans in predicting histological grade of HCC.

METHODS

Totally, 52 patients with HCC were enrolled in this retrospective study and underwent triple-phase enhanced CT imaging. The blood perfusion parameters were derived from triple-phase CT scans. The relationship of liver perfusion parameters and corresponding histogram parameters with the histological grade of HCC was analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal ability of the parameters to predict the tumor histological grade.

RESULTS

The variance of arterial enhancement fraction (AEF) was significantly higher in HCCs without poorly differentiated components (NP-HCCs) than in HCCs with poorly differentiated components (P-HCCs). The difference in hepatic blood flow (HF) between total tumor and total liver flow (ΔHF = HFtumor - HFliver) and relative flow (rHF = ΔHF/HFliver) were significantly higher in NP-HCCs than in P-HCCs. The difference in portal vein blood supply perfusion (PVP) between tumor and liver tissue (ΔPVP) and the ΔPVP/liver PVP ratio (rPVP) were significantly higher in patients with NP-HCCs than in patients with P-HCCs. The area under ROC (AUC) of ΔPVP and rPVP were both 0.697 with a high sensitivity of 84.2% and specificity of only 56.2%. The ΔHF and rHF had a higher specificity of 87.5% with an AUC of 0.681 and 0.673, respectively. The combination of rHF and rPVP showed the highest AUC of 0.732 with a sensitivity of 57.9% and specificity of 93.8%. The combined parameter of ΔHF and rPVP, rHF and rPVP had the highest positive predictive value of 0.903, and that of rPVP and ΔPVP had the highest negative predictive value of 0.781.

CONCLUSION

Liver perfusion parameters and corresponding histogram parameters (including ΔHF, rHF, ΔPVP, rPVP, and AEFvariance) in patients with HCC derived from traditional triphasic CT scans may be helpful to non-invasively and pre-operatively predict the degree of the differentiation of HCC.

Subregion Radiomics Analysis to Display Necrosis After Hepatic Microwave Ablation-A Proof of Concept Study

OBJECTIVES

The aim of this study was to improve the visualization of coagulation necrosis after computed tomography (CT)-guided microwave ablation (MWA) in routine postablational imaging.

MATERIALS AND METHODS

Ten MWAs were performed in 8 pigs under CT guidance. After each ablation, we obtained contrast-enhanced CT scans in venous phase. Ablations were then resected as a whole, and histologic slices were obtained orthogonally through the ablation center. Subsequently, a vital stain was applied to the sections for visualization of coagulation necrosis. Computed tomography images were reformatted to match the histologic slices. Afterwards, quantitative imaging features were extracted from the subregions of all images, and binary classifiers were used to predict the presence of coagulation necrosis for each subregion. From this, heatmaps could be created, which visually represented the extent of necrosis in each CT image. Two independent observers evaluated the extent of coagulative necrosis between the heat maps and histological sections.

RESULTS

We applied 4 different classifiers, including a generalized linear mixed model (GLMM), a stochastic gradient boosting classifier, a random forest classifier, and a k-nearest neighbor classifier, out of which the GLMM showed the best performance to display coagulation necrosis. The GLMM resulted in an area under the curve of 0.84 and a Jaccard index of 0.6 between the generated heat map and the histologic reference standard as well as a good interobserver agreement with a Jaccard index of 0.9.

CONCLUSIONS

Subregion radiomics analysis may improve visualization of coagulation necrosis after hepatic MWA in an in vivo porcine model.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study

BACKGROUND

Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy.

PURPOSE

To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer.

MATERIAL AND METHODS

Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer-including arterial flow (AF), blood volume (BV), and permeability surface (PS)-were measured in two different ways: the region of interest incorporating the "local hot" with the highest enhancement and "cold spot" with the lowest enhancement; and "whole-tumor" measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups.

RESULTS

There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups (P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group (P <0.05), but the AF in whole-tumor regions was not different between the two groups (P > 0.05). There were no differences in BV and PS between the two groups (P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion.

CONCLUSION

vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC

PURPOSE

This study was designed to preoperatively predict microvascular invasion (MVI) of solitary small hepatocellular carcinoma (sHCC) by quantitative analysis of Gd-EOB-DTPA enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI).

METHOD

Sixty-one patients, 19 with and 42 without histologically confirmed MVI following hepatic resection for solitary sHCC (≤ 3 cm), were preoperatively examined with Gd-EOB-DTPA-enhanced MRI. The regions of interest (ROIs) of the hepatic lesions were manually delineated on the maximum cross-sectional area in the HBP images and used to calculate the lesion boundary index (LBI) and marginal gray changes (MGC). Histogram analysis was performed to measure standard deviations (STD) and coefficients of variation (CV). Correlations between quantitative parameters and MVI were evaluated and differences between MVI positive and negative groups were assessed.

RESULTS

The average LBI (0.85 ± 0.07) and MGC (0.48 ± 0.27) values of the negative group were significantly higher (p < 0.05) than the corresponding LBI (0.72 ± 0.07) and MGC (0.28 ± 0.18) values of the positive group. STDs and CVs in the negative group were significantly smaller (p < 0.05) than those of the positive group. Receiver operating characteristic (ROC) analysis revealed that LBI had the best predictive value with an AUC, sensitivity, and specificity of 0.91, 87 %, and 80 %, respectively.

CONCLUSIONS

Quantitative analysis of HBP images is useful for predicting MVI and beneficial to clinicians in making decisions before treatment.

Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning

PURPOSE

Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively.

METHODS

In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models.

RESULTS

Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923-0.973) and 0.980 (95% CI 0.959-0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797-0.947) and 0.906 (95% CI 0.821-0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027).

CONCLUSION

The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.

Dynamic CT angiography for therapy evaluation after transarterial chemoembolization of hepatocellular carcinoma

Background

Liver dynamic computed tomography (CT) is an established method for pre- and post-interventional evaluation of hepatocellular carcinoma. To date only the liver parenchyma and perfusion information of dynamic CT has been evaluated widely.

Purpose

To evaluate the vascular information contained in dynamic CT datasets.

Material and Methods

Dynamic CT performed one day after transarterial chemoembolization (60 mL of contrast medium, 6 mL/s, 40 s scan duration) were retrospectively evaluated. Conventional slice and angiographic maximum-intensity-projection reconstructions were calculated on a multi-modality post-processing platform. Datasets were evaluated for viable tumor, anatomy of the vasculature, and potential tumor-feeding vessels. The results were compared to digital subtraction angiography images.

Results

In total, 94 treated hepatocellular carcinoma nodules were evaluated (62 dynamic CT scans, 46 patients [34 men; mean age = 69 years]). Forty-six partially viable tumors were diagnosed after transarterial chemoembolization. In all of these, tumor-feeding vessels were found in dynamic CT. Seventeen suspected extra-hepatic tumor feeders were reported, of which 14 had not been found during previous transarterial chemoembolization.

Conclusion

Dynamic CT is useful in post-interventional imaging of hepatocellular carcinoma after transarterial chemoembolization due to its ability to detect residual viable tumor parts and to show previously unknown intra- and extra-hepatic tumor-feeding vessels.

Imaging Features of the Whole Uterus Volume CT Perfusion and Influence Factors of Blood Supply: A Primary Study in Patients with Cervical Squamous Carcinoma

RATIONALE AND OBJECTIVES

To explore the imaging features of whole uterus volume CT perfusion (vCTP) and the influence factors of blood supply in cervical squamous carcinoma (CSC).

MATERIALS AND METHODS

vCTP was performed on a 640-slice computed tomography system in 43 patients with CSC diagnosed by biopsy, and 24 cases of them underwent magnetic resonance imaging. The size of the tumor was measured on vCTP and magnetic resonance (MR) images. Perfusion parameters, including arterial blood flow (AF), blood volume, and permeability surface (PS), were measured by two radiologists, using interclass correlation coefficient to evaluate the interobserver reliability. The difference of tumor size and perfusion data was analyzed by paired t test and rank sum test. The correlation of perfusion parameters with some factors was analyzed by Pearson or Spearman correlation analysis.

RESULTS

Tumor sizes were not significantly different between vCTP and MR images. The interclass correlation coefficient of each parameter was 0.818-0.945. The AF value of CSC was significantly higher than normal uterine body, and the blood volume and PS values of CSC were not statistically different compared with those of normal uterine body. There was no significant difference in AF value of CSC among different FIGO stages and pathological grades. The AF and PS values of CSC were negatively correlated with the age of the patients.

CONCLUSION

The vCTP could accurately shows the size of the CSC with use of MR as the reference standard, and its perfusion parameters have good measurement stability; the CSC was hypervascular, but this trend was less pronounced in older women.

ADC similarity predicts microvascular invasion of bifocal hepatocellular carcinoma

PURPOSE

This study aimed to investigate whether ADC similarity can predict microvascular invasion (MVI) in patients with bifocal hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Between January 2015 and September 2015, 51 patients with two HCC lesions were included. All patients underwent conventional magnetic resonance imaging including diffusion-weighted imaging (DWI) before the HCC lesions were surgically resected; the tumor specimens were examined histopathologically. Similarity between two HCC lesions regarding DWI signal intensity (SI) and ADC value was calculated as the difference between the two lesions: Value Similarity = [1-(|value_{large lesion}-value_{small lesion}|)/(value_{large lesion} + value_{small lesion})] × 100%. Univariate and multivariate logistic regression analyses were performed to assess the presence of MVI.

RESULTS

Risk factors significantly related to MVI of bifocal HCC in univariate analysis were cirrhosis (P = 0.010), histological grade (P = 0.040), DWI SI similarity (P = 0.027) and ADC similarity (P = 0.003). In multivariate analysis, cirrhosis (odds ratio 0.068, P = 0.022) and ADC similarity (odds ratio 1.204, P = 0.008) were independent risk factors for MVI of bifocal HCC.

CONCLUSION

In patients with two HCC lesions, highly similar ADC values for the two HCC lesions may be a preoperative predictor of MVI.

Preoperative computed tomography and serum α-fetoprotein to predict microvascular invasion in hepatocellular carcinoma

To determine the diagnostic value of computed tomography (CT) for prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Preoperative CTs for 160 patients with 57 MVI-positive and 103 MVI-negative HCCs diagnosed by surgical pathology were reviewed retrospectively. CT parameters and serum α-fetoprotein (AFP) level were analyzed in SPSS 16.0. Although univariate analysis showed that tumor size (P = .012), grade (Z = -2.114, P = .034), and peritumoral enhancement (χ = 4.464, P = .035) were associated with MVI, multiple logistic regression analysis showed that capsular invasion (odds ratio [OR] = 23.469, P < .001), margins (OR = 6.751, P < .001), and serum AFP level (OR = 1.001, P = .038) were associated with MVI in HCC (P < .05). Radiographic hepatic capsular invasion and nonsmooth tumor margins identified by preoperative CT images, along with AFP levels greater than 232.2 ng/mL, are important predictors of MVI.

Assessment of Microvascular Invasion of Hepatocellular Carcinoma with Diffusion Kurtosis Imaging

Purpose To evaluate the potential role of diffusion kurtosis imaging and conventional magnetic resonance (MR) imaging findings including standard monoexponential model of diffusion-weighted imaging and morphologic features for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Materials and Methods Institutional review board approval and written informed consent were obtained. Between September 2015 and November 2016, 84 patients (median age, 54 years; range, 29-79 years) with 92 histopathologically confirmed HCCs (40 MVI-positive lesions and 52 MVI-negative lesions) were analyzed. Preoperative MR imaging examinations including diffusion kurtosis imaging (b values: 0, 200, 500, 1000, 1500, and 2000 sec/mm²) were performed and kurtosis, diffusivity, and apparent diffusion coefficient maps were calculated. Morphologic features of conventional MR images were also evaluated. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of these parameters as potential predictors of MVI. Results Features significantly related to MVI of HCC at univariate analysis were increased mean kurtosis value (P < .001), decreased mean diffusivity value (P = .033) and apparent diffusion coefficient value (P = .011), and presence of infiltrative border with irregular shape (P = .005) and irregular circumferential enhancement (P = .026). At multivariate analysis, mean kurtosis value (odds ratio, 6.25; P = .001), as well as irregular circumferential enhancement (odds ratio, 6.92; P = .046), were independent risk factors for MVI of HCC. The mean kurtosis value for MVI of HCC showed an area under the receiver operating characteristic curve of 0.784 (optimal cutoff value was 0.917). Conclusion Higher mean kurtosis values in combination with irregular circumferential enhancement are potential predictive biomarkers for MVI of HCC. ^© RSNA, 2017.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT)

Introduction

Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available.

Objectives

ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction.

Discussion

This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

320-row CT renal perfusion imaging in patients with aortic dissection: A preliminary study

OBJECTIVE

To investigate the clinical value of renal perfusion imaging in patients with aortic dissection (AD) using 320-row computed tomography (CT), and to determine the relationship between renal CT perfusion imaging and various factors of aortic dissection.

METHODS

Forty-three patients with AD who underwent 320-row CT renal perfusion before operation were prospectively enrolled in this study. Diagnosis of AD was confirmed by transthoracic echocardiography. Blood flow (BF) of bilateral renal perfusion was measured and analyzed. CT perfusion imaging signs of AD in relation to the type of AD, number of entry tears and the false lumen thrombus were observed and compared.

RESULTS

The BF values of patients with type A AD were significantly lower than those of patients with type B AD (P = 0.004). No significant difference was found in the BF between different numbers of intimal tears (P = 0.288), but BF values were significantly higher in cases with a false lumen without thrombus and renal arteries arising from the true lumen than in those with thrombus (P = 0.036). The BF values measured between the true lumen, false lumen and overriding groups were different (P = 0.02), with the true lumen group having the highest. Also, the difference in BF values between true lumen and false lumen groups was statistically significant (P = 0.016), while no statistical significance was found in the other two groups (P > 0.05). The larger the size of intimal entry tears, the greater the BF values (P = 0.044).

CONCLUSIONS

This study shows a direct correlation between renal CT perfusion changes and AD, with the size, number of intimal tears, different types of AD, different renal artery origins and false lumen thrombosis, significantly affecting the perfusion values.

Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation

Microvascular invasion (MVI) is well known to negatively influence outcomes following surgical treatment of hepatocellular cancer (HCC) patients. The aim of this study was to evaluate the rationale for prediction of MVI before liver transplantation (LT). Data of 200 HCC patients after LT were subject to retrospective analysis. MVI was present in 57 patients (28.5%). Tumor number (p = 0.001) and size (p = 0.009), and alpha-fetoprotein (p = 0.049) were independent predictors of MVI used to create a prediction model, defined as: 0.293x(tumor number) + 0.283x(tumor size in cm) + 0.164xlog_e(alpha-fetoprotein in ng/ml) (c statistic  =  0.743). The established cut-off (≥2.24) was associated with sensitivity and specificity of 72%. MVI was not an independent risk factor for recurrence (p = 0.307), in contrast to tumor number (p = 0.047) and size (p < 0.001), alpha-fetoprotein (p < 0.001) and poor differentiation (p = 0.039). Recurrence-free survival at 5 years for patients without MVI was 85.9% as compared to 83.3% (p = 0.546) and 55.3% (p = 0.001) for patients with false negative and true positive prediction of MVI, respectively. The use of both morphological and biological tumor features enables effective pre-transplant prediction of high-risk MVI. Provided that these parameters are combined in selection of HCC patients for LT, pre-transplant identification of all patients with MVI does not appear necessary.

Detection and differentiation of early hepatocellular carcinoma from cirrhosis using CT perfusion in a rat liver model

BACKGROUND

Functional imaging such as CT perfusion can detect morphological and hemodynamic changes in hepatocellular carcinoma (HCC). Pre-carcinoma and early HCC nodules are difficult to differentiate by observing only their hemodynamics changes. The present study aimed to investigate hemodynamic parameters and evaluate their differential diagnostic cut-off between pre-carcinoma and early HCC nodules using CT perfusion and receiver operating characteristic (ROC) curves.

METHODS

Male Wistar rats were randomly divided into control (n=20) and experimental (n=70) groups. Diethylnitrosamine (DEN) was used to induce pre-carcinoma and early HCC nodules in the experimental group. Perfusion scanning was carried out on all survival rats discontinuously from 8 to 16 weeks. Hepatic portal perfusion (HPP), hepatic arterial fraction (HAF), hepatic arterial perfusion (HAP), hepatic blood volume (HBV), hepatic blood flow (HBF), mean transit time (MTT) and permeability of capillary vessel surface (PS) data were provided by mathematical deconvolution model. The perfusion parameters were compared among the three groups of rats (control, pre-carcinoma and early HCC groups) using the Kruskal-Wallis test and analyzed with ROC curves. Histological examination of the liver tissues with hematoxylin and eosin staining was performed after CT scan.

RESULTS

For HPP, HAF, HBV, HBF and MTT, there were significant differences among the three groups (P<0.05). HAF had the highest areas under the ROC curves: 0.80 (control vs pre-carcinoma groups) and 0.95 (control vs early HCC groups) with corresponding optimal cut-offs of 0.37 and 0.42, respectively. The areas under the ROC curves for HPP was 0.79 (control vs pre-carcinoma groups) and 0.92 (control vs early HCC groups) with corresponding optimal cut-offs of 136.60 mL/min/100 mg and 108.47 mL/min/100 mg, respectively.

CONCLUSIONS

CT perfusion combined with ROC curve analysis is a new diagnosis model for distinguishing between pre-carcinoma and early HCC nodules. HAF and HPP are the ideal reference indices.

Microvascular invasion in hepatocellular carcinoma: is it predictable with a new, preoperative application of diffusion-weighted imaging?

PURPOSE

The study aimed to explore the use of MRI in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

The preoperative MRI and tissues of resected HCC patients were collected. The imaging characteristics that have previously been suggested and the mismatch between diffusion-weighted imaging (DWI) and T2-weighted imaging of regions, which the authors called DWI/T2 mismatch, were analyzed and compared with histopathological references.

RESULTS

A multivariate logistic regression analysis showed that DWI/T2 mismatch was an independent predictor of MVI.

CONCLUSION

The DWI/T2 mismatch can be a preoperative predictor of MVI for HCC.

Parenchymal Blood Volume Assessed by C-Arm-Based Computed Tomography in Immediate Posttreatment Evaluation of Drug-Eluting Bead Transarterial Chemoembolization in Hepatocellular Carcinoma

OBJECTIVES

The aim of this study was to assess clinical utility of the quantitative perfusion parameter called parenchymal blood volume (PBV), as derived from C-arm-based computed tomography (CT), for immediate posttreatment assessment of drug-eluting bead (DEB) transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Twenty-four patients with early- or intermediate-stage HCC received DEB-TACE. A total of 52 HCC lesions were treated and assessed by C-arm CT before and after intervention. C-arm CT consisted of nonenhanced and contrast-enhanced acquisitions; from these, PBV maps were reconstructed. Lesion diameter, maximum PBV, and unenhanced parenchyma density were assessed before and after treatment. Diameter of visible contrast media deposits as well as residual vascularization was assessed after delivery of DEB. All patients underwent follow-up using cross-sectional imaging. All assessed lesions were evaluated concerning modified Response Evaluation Criteria in Solid Tumors for HCC.

RESULTS

All treated lesions showed significant decrease in PBV after DEB-TACE (mean difference, -15.61 mL/100 mL, P < 0.0001). Eleven lesions showed residual tumoral perfusion in PBV maps associated with an unfavorable outcome compared with completely treated lesions in terms of a lower tumor shrinkage over time (-0.02 ± 0.49 vs -0.76 ± 0.38; P < 0.0001). A contrast media deposit was seen in 78% of treated HCC lesions with a tendency toward better visibility in encapsulated lesions. Nonenhanced parenchyma density was significantly higher in all treated segments (149.69 ± 58.6 vs 68.42 ± 18.04, P < 0.0001).

CONCLUSIONS

Parenchymal blood volume values as derived from C-arm CT acquisitions in combination with nonenhanced and contrast-enhanced C-arm CT images are useful in posttreatment assessment of DEB-TACE in HCC. Residual tumor perfusion in PBV maps have predictive potential for mid-term tumor response in HCC and could allow a more individualized treatment schedule for DEB-TACE in HCC patients.

Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma

The objective is to explore the value of preoperative magnetic resonance (MR) histogram analyses in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Fifty-one patients with histologically confirmed HCC who underwent diffusion-weighted and contrast-enhanced MR imaging were included. Histogram analyses were performed and mean, variance, skewness, kurtosis, 1th, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared between HCCs with and without MVI. Receiver operating characteristics (ROC) analyses were generated to compare the diagnostic performance of tumor size, histogram analyses of apparent diffusion coefficient (ADC) maps, and MR enhancement.The mean, 1th, 10th, and 50th percentiles of ADC maps, and the mean, variance. 1th, 10th, 50th, 90th, and 99th percentiles of the portal venous phase (PVP) images were significantly different between the groups with and without MVI (P <0.05), with area under the ROC curves (AUCs) of 0.66 to 0.74 for ADC and 0.76 to 0.88 for PVP. The largest AUC of PVP (1th percentile) showed significantly higher accuracy compared with that of arterial phase (AP) or tumor size (P <0.001).MR histogram analyses-in particular for 1th percentile for PVP images-held promise for prediction of MVI of HCC.

Real-Time In Vivo Characterization of Primary Liver Tumors With Diffuse Optical Spectroscopy During Percutaneous Needle Interventions: Feasibility Study in Woodchucks

OBJECTIVE

This study presents the first in vivo real-time optical tissue characterization during image-guided percutaneous intervention using near-infrared diffuse optical spectroscopy sensing at the tip of a needle. The goal of this study was to indicate transition boundaries from healthy tissue to tumors, namely, hepatic carcinoma, based on the real-time feedback derived from the optical measurements.

MATERIALS AND METHODS

Five woodchucks with hepatic carcinoma were used for this study. The woodchucks were imaged with contrast-enhanced cone beam computed tomography with a flat panel detector C-arm system to visualize the carcinoma in the liver. In each animal, 3 insertions were performed, starting from the skin surface toward the hepatic carcinoma under image guidance. In 2 woodchucks, each end point of the insertion was confirmed with pathologic examination of a biopsy sample. While advancing the needle in the animals under image guidance such as fluoroscopy overlaid with cone beam computed tomography slice and ultrasound, optical spectra were acquired at the distal end of the needles. Optical tissue characterization was determined by translating the acquired optical spectra into clinical parameters such as blood, water, lipid, and bile fractions; tissue oxygenation levels; and scattering amplitude related to tissue density. The Kruskal-Wallis test was used to study the difference in the derived clinical parameters from the measurements performed within the healthy tissue and the hepatic carcinoma. Kurtoses were calculated to assess the dispersion of these parameters within the healthy and carcinoma tissues.

RESULTS

Blood and lipid volume fractions as well as tissue oxygenation and reduced scattering amplitude showed to be significantly different between the healthy part of the liver and the hepatic carcinoma (P < 0.05) being higher in normal liver tissue. A decrease in blood and lipid volume fractions and tissue oxygenation as well as an increase in scattering amplitude were observed when the tip of the needle crossed the margin from the healthy liver tissue to the carcinoma. The kurtosis for each derived clinical parameter was high in the hepatic tumor as compared with that in the healthy liver indicating intracarcinoma variability.

CONCLUSIONS

Tissue blood content, oxygenation level, lipid content, and tissue density all showed significant differences when the needle tip was guided from the healthy tissue to the carcinoma and can therefore be used to identify tissue boundaries during percutaneous image-guided interventions.

Imaging Hepatocellular Carcinoma with Dynamic CT Before and After Transarterial Chemoembolization: Optimal Scan Timing of Arterial Phase

RATIONALE AND OBJECTIVES

The aim of this study was to determine the optimal arterial phase delay for computed tomography imaging of hepatocellular carcinoma (HCC) before and after transarterial chemoembolization (TACE) using a low iodine dose protocol.

MATERIALS AND METHODS

A total of 39 patients with known HCC were imaged with dynamic computed tomography of the liver (40-second scan duration, 60 mL of contrast medium), both on the same day before TACE and 1 day after TACE. Time attenuation curves of vessels, nonmalignant liver parenchyma, and 62 HCCs were normalized to a uniform aortic contrast arrival and analyzed.

RESULTS

Maximal arterial phase HCC to liver contrast was reached between 13 and 17 seconds after aortic contrast arrival, both before and after TACE.

CONCLUSIONS

Using our low iodine dose protocol, arterial phase imaging of HCC should be performed between 13 and 17 seconds after aortic contrast arrival, both before and after TACE.